Data Augmentation

Data augmentation involves techniques used for increasing the amount of data, based on different modifications, to expand the amount of examples in the original dataset. Data augmentation not only helps to grow the dataset but it also increases the diversity of the dataset. When training machine learning models, data augmentation acts as a regularizer and helps to avoid overfitting.

Data augmentation techniques have been found useful in domains like NLP and computer vision. In computer vision, transformations like cropping, flipping, and rotation are used. In NLP, data augmentation techniques can include swapping, deletion, random insertion, among others.

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 7401–7425 of 8378 papers

Title	Date	Tasks	Status	Hype
Improving singing voice separation with the Wave-U-Net using Minimum Hyperspherical Energy	Oct 22, 2019	Data Augmentationimage-classification	CodeCode Available	0
Using Speech Synthesis to Train End-to-End Spoken Language Understanding Models	Oct 21, 2019	Data AugmentationNatural Language Understanding	CodeCode Available	2
Boosting Mapping Functionality of Neural Networks via Latent Feature Generation based on Reversible Learning	Oct 21, 2019	Data AugmentationFace Recognition	—Unverified	0
MIScnn: A Framework for Medical Image Segmentation with Convolutional Neural Networks and Deep Learning	Oct 21, 2019	Data AugmentationImage Segmentation	CodeCode Available	0
Cascaded Generation of High-quality Color Visible Face Images from Thermal Captures	Oct 21, 2019	Data AugmentationFace Alignment	—Unverified	0
Good, Better, Best: Textual Distractors Generation for Multiple-Choice Visual Question Answering via Reinforcement Learning	Oct 21, 2019	Data AugmentationDecision Making	—Unverified	0
Label-efficient audio classification through multitask learning and self-supervision	Oct 19, 2019	Audio ClassificationClassification	—Unverified	0
MonaLog: a Lightweight System for Natural Language Inference Based on Monotonicity	Oct 19, 2019	Data AugmentationNatural Language Inference	CodeCode Available	0
Real-Time Lip Sync for Live 2D Animation	Oct 19, 2019	Constrained Lip-synchronizationData Augmentation	CodeCode Available	0
Towards More Sample Efficiency in Reinforcement Learning with Data Augmentation	Oct 19, 2019	Data AugmentationDeep Reinforcement Learning	CodeCode Available	0
Illumination-Based Data Augmentation for Robust Background Subtraction	Oct 18, 2019	Data AugmentationForeground Segmentation	CodeCode Available	0
Automatic Data Augmentation by Learning the Deterministic Policy	Oct 18, 2019	Data AugmentationDeep Reinforcement Learning	CodeCode Available	0
Anatomically-Informed Data Augmentation for functional MRI with Applications to Deep Learning	Oct 17, 2019	Data AugmentationHyperparameter Optimization	—Unverified	0
ODE guided Neural Data Augmentation Techniques for Time Series Data and its Benefits on Robustness	Oct 15, 2019	Adversarial AttackData Augmentation	—Unverified	0
Self-supervised Label Augmentation via Input Transformations	Oct 14, 2019	Data Augmentationimbalanced classification	CodeCode Available	0
Sketch-Specific Data Augmentation for Freehand Sketch Recognition	Oct 14, 2019	Data AugmentationImage Retrieval	—Unverified	0
Generative Image Translation for Data Augmentation in Colorectal Histopathology Images	Oct 13, 2019	Data Augmentationimage-classification	CodeCode Available	0
Cross-Domain Image Classification through Neural-Style Transfer Data Augmentation	Oct 12, 2019	ClassificationData Augmentation	CodeCode Available	0
Towards DeepSpray: Using Convolutional Neural Network to post-process Shadowgraphy Images of Liquid Atomization	Oct 11, 2019	Data Augmentation	—Unverified	0
Adversarial Pulmonary Pathology Translation for Pairwise Chest X-ray Data Augmentation	Oct 11, 2019	Data AugmentationImage-to-Image Translation	CodeCode Available	0
Network Randomization: A Simple Technique for Generalization in Deep Reinforcement Learning	Oct 11, 2019	Data AugmentationDeep Reinforcement Learning	CodeCode Available	0
Efficient and Adaptive Kernelization for Nonlinear Max-margin Multi-view Learning	Oct 11, 2019	Data AugmentationMULTI-VIEW LEARNING	—Unverified	0
First Order Ambisonics Domain Spatial Augmentation for DNN-based Direction of Arrival Estimation	Oct 10, 2019	Data AugmentationDirection of Arrival Estimation	—Unverified	0
Unconstrained Road Marking Recognition with Generative Adversarial Networks	Oct 10, 2019	Data AugmentationDeblurring	—Unverified	0
A Closer Look At Feature Space Data Augmentation For Few-Shot Intent Classification	Oct 9, 2019	Data AugmentationGeneral Classification	—Unverified	0

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All datasets ImageNet CIFAR-10 GA1457

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	DeiT-B (+MixPro)	Accuracy (%)	82.9	—	Unverified
2	ResNet-200 (DeepAA)	Accuracy (%)	81.32	—	Unverified
3	DeiT-S (+MixPro)	Accuracy (%)	81.3	—	Unverified
4	ResNet-200 (Fast AA)	Accuracy (%)	80.6	—	Unverified
5	ResNet-200 (UA)	Accuracy (%)	80.4	—	Unverified
6	ResNet-200 (AA)	Accuracy (%)	80	—	Unverified
7	ResNet-50 (DeepAA)	Accuracy (%)	78.3	—	Unverified
8	ResNet-50 (TA wide)	Accuracy (%)	78.07	—	Unverified
9	ResNet-50 (LoRot-E)	Accuracy (%)	77.72	—	Unverified
10	ResNet-50 (LoRot-I)	Accuracy (%)	77.71	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	WideResNet-40-2 (Faster AA)	Percentage error	3.7	—	Unverified
2	Shake-Shake (26 2×32d) (Faster AA)	Percentage error	2.7	—	Unverified
3	WideResNet-28-10 (Faster AA)	Percentage error	2.6	—	Unverified
4	Shake-Shake (26 2×96d) (Faster AA)	Percentage error	2	—	Unverified
5	Shake-Shake (26 2×112d) (Faster AA)	Percentage error	2	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	DiffAug	Classification Accuracy	92.7	—	Unverified
2	PaCMAP	Classification Accuracy	85.3	—	Unverified
3	hNNE	Classification Accuracy	77.4	—	Unverified
4	TopoAE	Classification Accuracy	74.6	—	Unverified